Cancer therapy optimization is an issue that can be solved using the control engineering approach. An optimal therapy generation algorithm is presented and tested using a tractable mouse model of breast cancer. The optimized therapeutic protocol is calculated in a closed‐loop manner at fixed time instants, twice in a week. The controller consists of a nonlinear model predictive controller which uses the state estimation of a moving horizon estimator. The estimator also computes parameter estimates of the prediction model such that the time varying nature of tumor evolution can be captured. Results show that remission can be induced in a 28‐day interval using the algorithm.

This study evaluated and integrated automatic pathology equipment and a collaborative robot to create a fully autonomous workflow. We selected the Gemini AS Automated Slide Stainer, ClearVue Coverslipper, and Pannoramic 1000 digital slide scanner, controlled by a UR5e robotic arm. To perform essential clinical laboratory tasks, we determined that the robotic arm, in combination with a custom manipulator, requires 9 degrees of freedom—5 from the robot and 4 from the manufactured manipulator. The patented manipulator is equipped with a camera, LED lighting, and three specialized grippers for object detection and precise handling of equipment doors, magazines, and slides. It is designed to mount onto a standardized robot flange interface (ISO 9409-1-50-4-M6), making it mechanically compatible with various robot arms. A minimum of 24 distinct laboratory tasks were defined for the training of the robotic arm. This autonomous workflow mitigates labor shortages and accelerates diagnostic processes by offloading repetitive tasks, thereby improving efficiency in pathology laboratories.

Type 1 Diabetes Mellitus (T1DM) is a disease where insulin production is obstructed in the pancreas, and exogenous administration of the hormone must be utilized. Automatic control of the administration can be achieved using the Artificial Pancreas (AP) concept, whose performance is heavily reliant on the underlying control algorithm. A Robust Fixed-Point Transformations (RFPT)-based control strategy was designed to automate the insulin delivery process, which incorporates a Sliding Mode Differentiator (SMD) to provide higher order derivatives of the blood glucose level. Inter-patient variability, carbohydrate disturbances, and real-life sampling were included in the validation of the method. Results showed that the algorithm could regulate the blood glucose level, with a significant overshoot at the beginning of the control action due to the adaptive nature of the controller. Results indicate that the design requires additional modifications to be feasible in practice, including an extended validation with more virtual patients and realistic simulation settings in the future. Nevertheless, the current control algorithm has several attractive features, which are discussed with respect to PID and Model Predictive Control (MPC).